{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3g6Xb7vS2mfo"
   },
   "source": [
    "### 1. Dataset Description  <a id='columns'>\n",
    "\n",
    "In this hackathon, you will be predicting the costs of used cars given the data collected from various sources and distributed across various locations in India.\n",
    "\n",
    "Let’s see if your data science skills can help you predict the price of a used car based on a given set of features discussed below.\n",
    "\n",
    "Size of training set: 6,019 records\n",
    "\n",
    "Size of test set: 1,234 records\n",
    "\n",
    "FEATURES:\n",
    "\n",
    "Name: The brand and model of the car.\n",
    "\n",
    "Location: The location in which the car is being sold or is available for purchase.\n",
    "\n",
    "Year: The year or edition of the model.\n",
    "\n",
    "Kilometers_Driven: The total kilometres driven in the car by the previous owner(s) in KM.\n",
    "\n",
    "Fuel_Type: The type of fuel used by the car.\n",
    "\n",
    "Transmission: The type of transmission used by the car.\n",
    "\n",
    "Owner_Type: Whether the ownership is Firsthand, Second hand or other.\n",
    "\n",
    "Mileage: The standard mileage offered by the car company in kmpl or km/kg\n",
    "\n",
    "Engine: The displacement volume of the engine in cc.\n",
    "\n",
    "Power: The maximum power of the engine in bhp.\n",
    "\n",
    "Seats: The number of seats in the car.\n",
    "\n",
    "New_Price: The price of a new car of the same model.\n",
    "\n",
    "Price: The price of the used car in INR Lakhs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas_profiling\\plot.py:15: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\n",
      "    \"__main__\", mod_spec)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\runpy.py\", line 85, in _run_code\n",
      "    exec(code, run_globals)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 486, in start\n",
      "    self.io_loop.start()\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 127, in start\n",
      "    self.asyncio_loop.run_forever()\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\asyncio\\base_events.py\", line 422, in run_forever\n",
      "    self._run_once()\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\asyncio\\base_events.py\", line 1432, in _run_once\n",
      "    handle._run()\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\asyncio\\events.py\", line 145, in _run\n",
      "    self._callback(*self._args)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 117, in _handle_events\n",
      "    handler_func(fileobj, events)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 450, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 480, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 432, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2662, in run_cell\n",
      "    raw_cell, store_history, silent, shell_futures)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2785, in _run_cell\n",
      "    interactivity=interactivity, compiler=compiler, result=result)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2903, in run_ast_nodes\n",
      "    if self.run_code(code, result):\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2963, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-1-1531cf384148>\", line 6, in <module>\n",
      "    get_ipython().run_line_magic('matplotlib', 'inline')\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2131, in run_line_magic\n",
      "    result = fn(*args,**kwargs)\n",
      "  File \"<decorator-gen-108>\", line 2, in matplotlib\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\magic.py\", line 187, in <lambda>\n",
      "    call = lambda f, *a, **k: f(*a, **k)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\magics\\pylab.py\", line 99, in matplotlib\n",
      "    gui, backend = self.shell.enable_matplotlib(args.gui)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3051, in enable_matplotlib\n",
      "    pt.activate_matplotlib(backend)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\", line 311, in activate_matplotlib\n",
      "    matplotlib.pyplot.switch_backend(backend)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\", line 231, in switch_backend\n",
      "    matplotlib.use(newbackend, warn=False, force=True)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\", line 1410, in use\n",
      "    reload(sys.modules['matplotlib.backends'])\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\importlib\\__init__.py\", line 166, in reload\n",
      "    _bootstrap._exec(spec, module)\n",
      "  File \"C:\\Users\\sandeep.rathod\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\__init__.py\", line 16, in <module>\n",
      "    line for line in traceback.format_stack()\n",
      "\n",
      "\n",
      "  matplotlib.use(BACKEND)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import pandas_profiling\n",
    "matplotlib.rc('xtick', labelsize=20) \n",
    "matplotlib.rc('ytick', labelsize=20)\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.metrics import f1_score, log_loss\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_selection import SelectKBest \n",
    "from sklearn.feature_selection import chi2\n",
    "import collections\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "from collections import Counter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_mode_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x1=pd.ExcelFile(r'Data_Train.xlsx')\n",
    "x2=pd.ExcelFile(r'Data_Test.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sheet1']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1.sheet_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create a list of six columns\n",
    "\n",
    "1. Name\n",
    "2. Location\n",
    "3. Year\n",
    "4. Kilometers_Driven\n",
    "5. Fuel_Type\n",
    "6. Transmission\n",
    "7. Owner_Type\n",
    "8. Mileage\n",
    "9. Engine\n",
    "10. Power\n",
    "11. Seats\n",
    "12. New_Price\n",
    "13. Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the columns to be read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_read=['Name','Location','Year','Kilometers_Driven','Fuel_Type','Transmission','Owner_Type','Mileage','Engine','Power'\n",
    "             ,'Seats','New_Price','Price']\n",
    "columns_readt=['Name','Location','Year','Kilometers_Driven','Fuel_Type','Transmission','Owner_Type','Mileage','Engine','Power'\n",
    "             ,'Seats','New_Price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### parse the data and read the six columns present in  sheet of data and skip first six rows and set skipfooter=14 and the column names as the list defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   New_Price  Price  \n",
       "0        NaN   1.75  \n",
       "1        NaN  12.50  \n",
       "2  8.61 Lakh   4.50  \n",
       "3        NaN   6.00  \n",
       "4        NaN  17.74  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_input = x1.parse(u'Sheet1',names=columns_read)\n",
    "ds_input.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Alto K10 LXI CNG</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2014</td>\n",
       "      <td>40929</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>32.26 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.2 bhp</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maruti Alto 800 2016-2019 LXI</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>54493</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>24.7 kmpl</td>\n",
       "      <td>796 CC</td>\n",
       "      <td>47.3 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota Innova Crysta Touring Sport 2.4 MT</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2017</td>\n",
       "      <td>34000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>13.68 kmpl</td>\n",
       "      <td>2393 CC</td>\n",
       "      <td>147.8 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.27 Lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota Etios Liva GD</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2012</td>\n",
       "      <td>139000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.59 kmpl</td>\n",
       "      <td>1364 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hyundai i20 Magna</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2014</td>\n",
       "      <td>29000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.5 kmpl</td>\n",
       "      <td>1197 CC</td>\n",
       "      <td>82.85 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name    Location  Year  \\\n",
       "0                    Maruti Alto K10 LXI CNG       Delhi  2014   \n",
       "1              Maruti Alto 800 2016-2019 LXI  Coimbatore  2013   \n",
       "2  Toyota Innova Crysta Touring Sport 2.4 MT      Mumbai  2017   \n",
       "3                       Toyota Etios Liva GD   Hyderabad  2012   \n",
       "4                          Hyundai i20 Magna      Mumbai  2014   \n",
       "\n",
       "   Kilometers_Driven Fuel_Type Transmission Owner_Type      Mileage   Engine  \\\n",
       "0              40929       CNG       Manual      First  32.26 km/kg   998 CC   \n",
       "1              54493    Petrol       Manual     Second    24.7 kmpl   796 CC   \n",
       "2              34000    Diesel       Manual      First   13.68 kmpl  2393 CC   \n",
       "3             139000    Diesel       Manual      First   23.59 kmpl  1364 CC   \n",
       "4              29000    Petrol       Manual      First    18.5 kmpl  1197 CC   \n",
       "\n",
       "       Power  Seats   New_Price  \n",
       "0   58.2 bhp    4.0         NaN  \n",
       "1   47.3 bhp    5.0         NaN  \n",
       "2  147.8 bhp    7.0  25.27 Lakh  \n",
       "3   null bhp    5.0         NaN  \n",
       "4  82.85 bhp    5.0         NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dst_input = x2.parse(u'Sheet1',names=columns_readt)\n",
    "dst_input.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#report = pandas_profiling.ProfileReport(ds_input)\n",
    "#covert profile report as html file\n",
    "#report.to_file(\"ds_input.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 13 columns):\n",
      "Name                 6019 non-null object\n",
      "Location             6019 non-null object\n",
      "Year                 6019 non-null int64\n",
      "Kilometers_Driven    6019 non-null int64\n",
      "Fuel_Type            6019 non-null object\n",
      "Transmission         6019 non-null object\n",
      "Owner_Type           6019 non-null object\n",
      "Mileage              6017 non-null object\n",
      "Engine               5983 non-null object\n",
      "Power                5983 non-null object\n",
      "Seats                5977 non-null float64\n",
      "New_Price            824 non-null object\n",
      "Price                6019 non-null float64\n",
      "dtypes: float64(2), int64(2), object(9)\n",
      "memory usage: 611.4+ KB\n"
     ]
    }
   ],
   "source": [
    " ds_input.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1234 entries, 0 to 1233\n",
      "Data columns (total 12 columns):\n",
      "Name                 1234 non-null object\n",
      "Location             1234 non-null object\n",
      "Year                 1234 non-null int64\n",
      "Kilometers_Driven    1234 non-null int64\n",
      "Fuel_Type            1234 non-null object\n",
      "Transmission         1234 non-null object\n",
      "Owner_Type           1234 non-null object\n",
      "Mileage              1234 non-null object\n",
      "Engine               1224 non-null object\n",
      "Power                1224 non-null object\n",
      "Seats                1223 non-null float64\n",
      "New_Price            182 non-null object\n",
      "dtypes: float64(1), int64(2), object(9)\n",
      "memory usage: 115.8+ KB\n"
     ]
    }
   ],
   "source": [
    "dst_input.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6019 entries, 0 to 6018\n",
      "Data columns (total 13 columns):\n",
      "Name                 6019 non-null object\n",
      "Location             6019 non-null object\n",
      "Year                 6019 non-null int64\n",
      "Kilometers_Driven    6019 non-null int64\n",
      "Fuel_Type            6019 non-null object\n",
      "Transmission         6019 non-null object\n",
      "Owner_Type           6019 non-null object\n",
      "Mileage              6017 non-null object\n",
      "Engine               5983 non-null object\n",
      "Power                5983 non-null object\n",
      "Seats                5977 non-null float64\n",
      "New_Price            824 non-null object\n",
      "Price                6019 non-null float64\n",
      "dtypes: float64(2), int64(2), object(9)\n",
      "memory usage: 658.3+ KB\n"
     ]
    }
   ],
   "source": [
    "#check duplicates\n",
    "ds_input =ds_input.drop_duplicates()\n",
    "ds_input.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining the train and test \n",
    "y_train = ds_input['Price']\n",
    "train_nomissing = ds_input.drop('Price',axis=1)\n",
    "test_nomissing = dst_input\n",
    "combined_set = pd.concat([train_nomissing, test_nomissing], axis = 0) # Stacks them vertically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7253 entries, 0 to 1233\n",
      "Data columns (total 12 columns):\n",
      "Name                 7253 non-null object\n",
      "Location             7253 non-null object\n",
      "Year                 7253 non-null int64\n",
      "Kilometers_Driven    7253 non-null int64\n",
      "Fuel_Type            7253 non-null object\n",
      "Transmission         7253 non-null object\n",
      "Owner_Type           7253 non-null object\n",
      "Mileage              7251 non-null object\n",
      "Engine               7207 non-null object\n",
      "Power                7207 non-null object\n",
      "Seats                7200 non-null float64\n",
      "New_Price            1006 non-null object\n",
      "dtypes: float64(1), int64(2), object(9)\n",
      "memory usage: 736.6+ KB\n"
     ]
    }
   ],
   "source": [
    " combined_set.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "#dropping  null places row.\n",
    "ds_input.drop(ds_input[ds_input['Place'].isnull()].index.tolist(),inplace=True)\n",
    "dst_input.drop(dst_input[dst_input['Place'].isnull()].index.tolist(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set[\"Power\"] = combined_set[\"Power\"].str.replace('null bhp','0 bhp')\n",
    "combined_set[\"Power\"].fillna('0 bhp',inplace=True)\n",
    "\n",
    "combined_set[\"Mileage\"].fillna('0.0 kmpl',inplace=True)\n",
    "\n",
    "\n",
    "#Seats\n",
    "combined_set[\"Engine\"].fillna('0 CC',inplace=True)\n",
    "\n",
    "combined_set[\"Seats\"].fillna(0,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spliting mileage\n",
    "ds_mil = combined_set[\"Mileage\"].str.split(\" \",n=1,expand=True)\n",
    "ds_mil1 = ds_mil[0].str.split(\".\",n=1,expand=True)\n",
    "combined_set[\"Mileage\"]=ds_mil1[0].astype(str).astype(int)\n",
    "#combined_set[\"Mileage\"]=combined_set[\"Mileage\"].str.replace('0','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_eng = combined_set[\"Engine\"].str.split(\" \",n=1,expand=True)\n",
    "combined_set[\"Engine\"]=ds_eng[0].astype(str).astype(int)\n",
    "#combined_set[\"Engine\"]=combined_set[\"Engine\"].str.replace('0','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_pwr = combined_set[\"Power\"].str.split(\" \",n=1,expand=True)\n",
    "ds_pwr1 = ds_pwr[0].str.split(\".\",n=1,expand=True)\n",
    "\n",
    "combined_set[\"Power\"]=ds_pwr1[0].astype(str).astype(int)\n",
    "#combined_set[\"Power\"]=combined_set[\"Power\"].str.replace('0','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set[\"New_Price\"]= combined_set[\"New_Price\"].replace(np.nan,'0 Lakh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_prc = combined_set[\"New_Price\"].str.split(\" \",n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set[\"cur\"]=ds_prc[1].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_prc1 = ds_prc[0].str.split(\".\",n=1,expand=True)\n",
    "\n",
    "combined_set[\"New_Price\"]=ds_prc1[0].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_cur(val_cur):\n",
    "    val,cur = val_cur\n",
    "    if cur=='Cr':\n",
    "        val = val*100\n",
    "    return val\n",
    "\n",
    "combined_set[\"New_Price\"] = combined_set[[\"New_Price\",'cur']].apply(cal_cur, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set[\"Name2\"]=combined_set[\"Name\"].apply(lambda x:' '.join(x.split()[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set[\"Name1\"]=combined_set[\"Name\"].apply(lambda x:' '.join(x.split()[:1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "combined_set['Name2'] = combined_set['Name2'].apply(lambda x: \" \".join(x.lower() for x in x.split()))\n",
    "combined_set['Name'] = combined_set['Name'].apply(lambda x: \" \".join(x.lower() for x in x.split()))\n",
    "combined_set['Name1'] = combined_set['Name1'].apply(lambda x: \" \".join(x.lower() for x in x.split()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>cur</th>\n",
       "      <th>Name2</th>\n",
       "      <th>Name1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>maruti wagon r lxi cng</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26</td>\n",
       "      <td>998</td>\n",
       "      <td>58</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>maruti wagon</td>\n",
       "      <td>maruti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hyundai creta 1.6 crdi sx option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19</td>\n",
       "      <td>1582</td>\n",
       "      <td>126</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>hyundai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>honda jazz v</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18</td>\n",
       "      <td>1199</td>\n",
       "      <td>88</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>honda jazz</td>\n",
       "      <td>honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga vdi</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20</td>\n",
       "      <td>1248</td>\n",
       "      <td>88</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>maruti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4 new 2.0 tdi multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15</td>\n",
       "      <td>1968</td>\n",
       "      <td>140</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>audi a4</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            maruti wagon r lxi cng      Mumbai  2010              72000   \n",
       "1  hyundai creta 1.6 crdi sx option        Pune  2015              41000   \n",
       "2                      honda jazz v     Chennai  2011              46000   \n",
       "3                 maruti ertiga vdi     Chennai  2012              87000   \n",
       "4   audi a4 new 2.0 tdi multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage  Engine  Power  Seats  New_Price  \\\n",
       "0       CNG       Manual      First       26     998     58    5.0          0   \n",
       "1    Diesel       Manual      First       19    1582    126    5.0          0   \n",
       "2    Petrol       Manual      First       18    1199     88    5.0          8   \n",
       "3    Diesel       Manual      First       20    1248     88    7.0          0   \n",
       "4    Diesel    Automatic     Second       15    1968    140    5.0          0   \n",
       "\n",
       "    cur          Name2    Name1  \n",
       "0  Lakh   maruti wagon   maruti  \n",
       "1  Lakh  hyundai creta  hyundai  \n",
       "2  Lakh     honda jazz    honda  \n",
       "3  Lakh  maruti ertiga   maruti  \n",
       "4  Lakh        audi a4     audi  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set[\"Power\"].replace(0,np.nan,inplace=True)\n",
    "combined_set[\"Mileage\"].replace(0,np.nan,inplace=True)\n",
    "combined_set[\"Engine\"].replace(0,np.nan,inplace=True)\n",
    "combined_set[\"Seats\"].replace(0,np.nan,inplace=True)\n",
    "combined_set[\"New_Price\"].replace(0,np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_Name=combined_set.groupby(['Name'])\n",
    "by_Name2=combined_set.groupby(['Name2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_Name1=combined_set.groupby(['Name1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_median(series):\n",
    "    return series.fillna(series.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set[\"Power\"]=by_Name['Power'].transform(impute_median)\n",
    "combined_set[\"Mileage\"]=by_Name['Mileage'].transform(impute_median)\n",
    "combined_set[\"Engine\"]=by_Name['Engine'].transform(impute_median)\n",
    "combined_set[\"Seats\"]=by_Name['Seats'].transform(impute_median)\n",
    "combined_set[\"New_Price\"]=by_Name['New_Price'].transform(impute_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set[\"Power\"]=by_Name2['Power'].transform(impute_median)\n",
    "combined_set[\"Mileage\"]=by_Name2['Mileage'].transform(impute_median)\n",
    "combined_set[\"Engine\"]=by_Name2['Engine'].transform(impute_median)\n",
    "combined_set[\"Seats\"]=by_Name2['Seats'].transform(impute_median)\n",
    "combined_set[\"New_Price\"]=by_Name2['New_Price'].transform(impute_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set[\"Power\"]=by_Name1['Power'].transform(impute_median)\n",
    "combined_set[\"Mileage\"]=by_Name1['Mileage'].transform(impute_median)\n",
    "combined_set[\"Engine\"]=by_Name1['Engine'].transform(impute_median)\n",
    "combined_set[\"Seats\"]=by_Name1['Seats'].transform(impute_median)\n",
    "#combined_set[\"New_Price\"]=by_Name1['New_Price'].transform(impute_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set['Mileage'].fillna(0,inplace= True)\n",
    "combined_set['Power'].fillna(0,inplace= True)\n",
    "combined_set['New_Price'].fillna(0,inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy1_combined_set=combined_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combined_set = dummy1_combined_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train =  pd.concat([combined_set[:train_nomissing.shape[0]],y_train], axis=1) # Up to the last initial training set row\n",
    "dst_input = combined_set[train_nomissing.shape[0]:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3024x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mx = sns.factorplot(y=\"Location\",x=\"Price\", data=df_train, kind=\"box\",col='Transmission', size=7,aspect=3)\n",
    "mx.set(ylabel='Price ')\n",
    "plt.title('Location Vs Price ');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mx = sns.factorplot(x=\"Year\", y=\"Price\", data=df_train, kind=\"box\", size=10 ,aspect=3)\n",
    "mx.set(xlabel= 'Year')\n",
    "#mx.set_xticklabels('Year',rotation=45)\n",
    "mx.set(ylabel= 'Price')\n",
    "plt.title('Year Vs Price');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,15))\n",
    "sns.lmplot(y='Kilometers_Driven',x='Price',data=df_train,fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,15))\n",
    "sns.lmplot(x='New_Price',y='Price',data=df_train,fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>cur</th>\n",
       "      <th>Name2</th>\n",
       "      <th>Name1</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4079</th>\n",
       "      <td>land rover range rover 3.0 diesel lwb vogue</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017</td>\n",
       "      <td>25000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2993.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Cr</td>\n",
       "      <td>land rover</td>\n",
       "      <td>land</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Name   Location  Year  \\\n",
       "4079  land rover range rover 3.0 diesel lwb vogue  Hyderabad  2017   \n",
       "\n",
       "      Kilometers_Driven Fuel_Type Transmission Owner_Type  Mileage  Engine  \\\n",
       "4079              25000    Diesel    Automatic      First     13.0  2993.0   \n",
       "\n",
       "      Power  Seats  New_Price cur       Name2 Name1  Price  \n",
       "4079  255.0    5.0      200.0  Cr  land rover  land  160.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.loc[(df_train['Price']>150) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_train =df_train.drop(df_train.loc[(df_train['Price']>150)].index.tolist()).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>cur</th>\n",
       "      <th>Name2</th>\n",
       "      <th>Name1</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>bmw x5 xdrive 30d m sport</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2017</td>\n",
       "      <td>6500000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2993.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>bmw x5</td>\n",
       "      <td>bmw</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name Location  Year  Kilometers_Driven Fuel_Type  \\\n",
       "2328  bmw x5 xdrive 30d m sport  Chennai  2017            6500000    Diesel   \n",
       "\n",
       "     Transmission Owner_Type  Mileage  Engine  Power  Seats  New_Price   cur  \\\n",
       "2328    Automatic      First     15.0  2993.0  258.0    5.0        0.0  Lakh   \n",
       "\n",
       "       Name2 Name1  Price  \n",
       "2328  bmw x5   bmw   65.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.loc[ (df_train['Kilometers_Driven']>6000000) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_train['Kilometers_Driven'] =df_train['Kilometers_Driven'].apply(lambda x: 650000 if x>6000000 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mx = sns.factorplot(y=\"Fuel_Type\", x=\"Price\", data=df_train, col='Transmission',kind=\"box\", size=5 ,aspect=2)\n",
    "mx.set(xlabel='Fuel_Type')\n",
    "mx.set(ylabel= 'Price')\n",
    "plt.title('Fuel_Type Vs Price');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train =df_train.drop(df_train.loc[(df_train['Fuel_Type']=='Electric') ].index.tolist()).reset_index(drop=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mx = sns.factorplot(x=\"Owner_Type\", y=\"Price\", data=df_train, kind=\"box\",col='Transmission', size=5 ,aspect=2)\n",
    "mx.set(xlabel='Owner_Type')\n",
    "mx.set(ylabel= 'Price')\n",
    "plt.title('Owner_Type Vs Price');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mx = sns.factorplot(x=\"Seats\", y=\"Price\", data=df_train, kind=\"box\",col='Transmission', size=5 ,aspect=2)\n",
    "mx.set(xlabel='Seats')\n",
    "mx.set(ylabel= 'Price')\n",
    "plt.title('Seats Vs Price');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mx = sns.factorplot(y=\"Name1\", x=\"Price\", data=df_train,kind=\"box\",size=7 ,aspect=3)\n",
    "mx.set(xlabel='Name1')\n",
    "mx.set(ylabel= 'Price')\n",
    "plt.title('Name1 Vs Price');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>cur</th>\n",
       "      <th>Name2</th>\n",
       "      <th>Name1</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>porsche cayenne base</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>2019</td>\n",
       "      <td>14298</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2995.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Cr</td>\n",
       "      <td>porsche cayenne</td>\n",
       "      <td>porsche</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name Location  Year  Kilometers_Driven Fuel_Type  \\\n",
       "3132  porsche cayenne base    Kochi  2019              14298    Petrol   \n",
       "\n",
       "     Transmission Owner_Type  Mileage  Engine  Power  Seats  New_Price cur  \\\n",
       "3132    Automatic      First     13.0  2995.0  340.0    5.0      100.0  Cr   \n",
       "\n",
       "                Name2    Name1  Price  \n",
       "3132  porsche cayenne  porsche   2.02  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.loc[ (df_train['Name1']=='porsche') & (df_train['Price']<3) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train =df_train.drop(df_train.loc[(df_train['Name1']=='porsche') & (df_train['Price']<3)].index.tolist()).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adding the name length to see how much does a long description influence the price\n",
    "df_train['namelen'] = [min(70, len(n)) for n in df_train['Name']]\n",
    "dst_input['namelen'] = [min(70, len(n)) for n in dst_input['Name']]\n",
    "ax = sns.jointplot(x='namelen', \n",
    "                   y='Price',\n",
    "                   data=df_train[['namelen','Price']], \n",
    "                    alpha=0.1, \n",
    "                    size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_df_train = pd.DataFrame(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCs9o0xt3Zfi"
   },
   "outputs": [],
   "source": [
    "price_df_train.to_excel('price_df_train.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2278dee2828>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(8,8))\n",
    "plt.title('Feature Correlation Heat Map')\n",
    "sns.heatmap(df_train.corr(),linewidths=.1,vmax=1.0,\n",
    "            square=True,linecolor='',annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>cur</th>\n",
       "      <th>Name2</th>\n",
       "      <th>Name1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>maruti wagon r lxi cng</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.0</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>maruti wagon</td>\n",
       "      <td>maruti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hyundai creta 1.6 crdi sx option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>hyundai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>honda jazz v</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>honda jazz</td>\n",
       "      <td>honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga vdi</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>maruti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4 new 2.0 tdi multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>audi a4</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            maruti wagon r lxi cng      Mumbai  2010              72000   \n",
       "1  hyundai creta 1.6 crdi sx option        Pune  2015              41000   \n",
       "2                      honda jazz v     Chennai  2011              46000   \n",
       "3                 maruti ertiga vdi     Chennai  2012              87000   \n",
       "4   audi a4 new 2.0 tdi multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage  Engine  Power  Seats  New_Price  \\\n",
       "0       CNG       Manual      First     26.0   998.0   58.0    5.0        5.0   \n",
       "1    Diesel       Manual      First     19.0  1582.0  126.0    5.0       15.5   \n",
       "2    Petrol       Manual      First     18.0  1199.0   88.0    5.0        8.0   \n",
       "3    Diesel       Manual      First     20.0  1248.0   88.0    7.0       11.0   \n",
       "4    Diesel    Automatic     Second     15.0  1968.0  140.0    5.0       53.0   \n",
       "\n",
       "    cur          Name2    Name1  \n",
       "0  Lakh   maruti wagon   maruti  \n",
       "1  Lakh  hyundai creta  hyundai  \n",
       "2  Lakh     honda jazz    honda  \n",
       "3  Lakh  maruti ertiga   maruti  \n",
       "4  Lakh        audi a4     audi  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining the train and test \n",
    "y_train = df_train['Price']\n",
    "train_nomissing = df_train.drop('Price',axis=1)\n",
    "test_nomissing = dst_input\n",
    "combined_set = pd.concat([train_nomissing, test_nomissing], axis = 0) # Stacks them vertically"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S44QmB3I2mg5"
   },
   "source": [
    "### 4. Feature Engineering  <a id='feature'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy2_combined_set=combined_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set = dummy2_combined_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# agegroup below 30 and above 59 as a categorical feature\n",
    "def premium(pro):\n",
    "    if(pro in [\"maruti\",\"hyundai\",'honda','ford','mahindra','tata','skoda','renault'\n",
    "              ,'chevrolet','nissan','fiat','datsun','ambassador','hindustan'\n",
    "              ,'Smart']):\n",
    "        pro='I'\n",
    "    else:\n",
    "        pro='P'\n",
    "    return pro\n",
    "      \n",
    "combined_set['is_premium'] = combined_set['Name1'].apply(premium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# agegroup below 30 and above 59 as a categorical feature\n",
    "def Exp_output(Ex):\n",
    "    if Ex<=2006:\n",
    "        Ex=2006\n",
    "    return Ex\n",
    "      \n",
    "combined_set['Year'] = combined_set['Year'].apply(Exp_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# agegroup below 30 and above 59 as a categorical feature\n",
    "def Exp_output(Ex):\n",
    "    if (Ex in ['CNG','LPG']):\n",
    "        Ex='LPG_CNG'\n",
    "    return Ex\n",
    "      \n",
    "combined_set['Fuel_Type'] = combined_set['Fuel_Type'].apply(Exp_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# agegroup below 30 and above 59 as a categorical feature\n",
    "def Exp_owner(Ex_Tr):\n",
    "    Ex,Tr=Ex_Tr\n",
    "    if ((Tr =='Manual' and Ex in ['Second','Third']) or (Ex=='Fourth & Above')):\n",
    "        Ex='com'\n",
    "    return Ex\n",
    "      \n",
    "combined_set['Owner_Type'] = combined_set[['Owner_Type','Transmission']].apply(Exp_owner,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# agegroup below 30 and above 59 as a categorical feature\n",
    "def Exp_price(Ex_pr):\n",
    "    Ex,pr=Ex_pr\n",
    "    if Ex in ['mercedes-benz s','jaguar f'] :\n",
    "        pr=200\n",
    "    elif Ex in ['audi a8','mercedes-benz sl-class']:\n",
    "        pr=100\n",
    "    elif Ex in ['mercedes-benz gl-class','bmw x6','jaguar xj','mercedes-benz slk-class','volvo xc90','audi a7',\n",
    "               'porsche cayman','toyota land'] :\n",
    "        pr=90\n",
    "    elif Ex in ['bmw x5','bmw 6','mitsubishi montero']:\n",
    "        pr=70\n",
    "    elif Ex in ['mercedes-benz m-class','audi tt','bmw z4'] :\n",
    "        pr=66\n",
    "    elif Ex in ['audi q5','volvo xc60','mercedes-benz r-class','volvo s80','nissan 370z']:\n",
    "        pr=55\n",
    "    elif Ex in ['honda accord']:\n",
    "        pr=44\n",
    "    elif Ex in ['hyundai santa','volkswagen passat','renault koleos','nissan x-trail','chevrolet captiva','bmw 1',\n",
    "               'mitsubishi outlander','fiat abarth']:\n",
    "        pr=30\n",
    "    elif Ex in[ 'mahindra ssangyong','skoda yeti','nissan teana','maruti grand','isuzu mu']:\n",
    "        pr= 24\n",
    "    elif Ex=='hyundai sonata':\n",
    "        pr=18\n",
    "    elif Ex in ['volkswagen jetta','isuzu d-max']:\n",
    "        pr=17\n",
    "    elif Ex in['skoda laura','chevrolet cruze','renault fluence']:\n",
    "        pr=16.5\n",
    "    elif Ex=='tata new':\n",
    "        pr=14\n",
    "    elif Ex in ['mahindra scorpio','renault scala','tata xenon','force one','chevrolet tavera','renault captur']:\n",
    "        pr=12\n",
    "    elif Ex in ['ford fiesta','maruti sx4','honda civic','mahindra tuv','chevrolet optra','maruti s',\n",
    "               'tata sumo','mahindra quanto','toyota qualis','volkswagen crosspolo','renault lodgy','mitsubishi lancer'\n",
    "               ,'mitsubishi cedia','mahindra jeep']:\n",
    "        pr=9\n",
    "    elif Ex in ['hyundai accent','honda mobilio','chevrolet enjoy','renault pulse','fiat grande','mahindra renault',\n",
    "               'ford classic']:\n",
    "        pr=7\n",
    "    elif Ex in ['maruti ritz','tata manza','chevrolet aveo','chevrolet sail','maruti esteem','mahindra logan',\n",
    "               'ford fusion','opelcorsa 1.4gsi']:\n",
    "        pr=6\n",
    "    elif Ex in ['tata indigo','hyundai i10','ford ikon','fiat petra','hindustan motors']:\n",
    "        pr= 5\n",
    "    elif Ex in ['hyundai eon','hyundai getz','chevrolet beat','tata venture']:\n",
    "        pr=4.5\n",
    "    elif Ex in['tata indica','maruti a-star','maruti estilo','maruti versa','maruti 1000','datsun redi']:\n",
    "        pr=4\n",
    "    elif Ex in ['chevrolet spark','maruti zen']:\n",
    "        pr=3.5\n",
    "    elif Ex in ['maruti omni','maruti 800']:\n",
    "        pr=3\n",
    "    elif Ex =='tata nano':\n",
    "        pr=2.5     \n",
    "    return pr\n",
    "      \n",
    "combined_set['New_Price'] = combined_set[['Name2','New_Price']].apply(Exp_price,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding new feature for variant\n",
    "\n",
    "def variant(pro):\n",
    "    Ex = 1\n",
    "    if ('vdi' in pro or 'vxi' in pro or 'at' in pro or 'xline' in pro or 'sportline' in pro or 'lt' in pro or 'ltz' in pro\n",
    "       or 'sport' in pro or 'sportz' in pro or 'sunroof' in pro or 'trent' in pro or 'Technology' in pro\n",
    "       or 'Technology' in pro or 'Design' in pro or 'expedition' in pro or 'vx' in pro  or 'magna' in pro):\n",
    "        Ex=2\n",
    "    elif ('zdi' in pro or 'zxi' in pro or 'exclusive' in pro or 'luxury' in pro or 'plus' in pro or 'roadster' in pro\n",
    "         or 'limited' in pro or 'Premium' in pro ):\n",
    "        Ex=3\n",
    "    return float(Ex)\n",
    "      \n",
    "combined_set['variant'] = combined_set['Name'].apply(variant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set.to_excel('combined_set3.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "combined_set=combined_set.drop(['Name','Name1','cur'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "combined_set=pd.get_dummies(data=combined_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_dummies = pd.get_dummies(combined_set.Seats, prefix='Seats')\n",
    "combined_set = pd.concat([combined_set,st_dummies], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_set=combined_set.drop(['Seats'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combined_set.to_excel('combined_set.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gYToxz3a2Jf4"
   },
   "source": [
    "### Scaling dataset   <a id='pcalr'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4m785xRv2Jf4",
    "outputId": "18cdca9f-7884-44ac-f29e-ae04150d3402"
   },
   "outputs": [],
   "source": [
    "df_train = combined_set[:train_nomissing.shape[0]] # Up to the last initial training set row\n",
    "df_test = combined_set[train_nomissing.shape[0]:] # Past the last initial training set row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4m785xRv2Jf4",
    "outputId": "18cdca9f-7884-44ac-f29e-ae04150d3402"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "df_train_arr_scld = sc.fit_transform(df_train)\n",
    "df_train_scld=pd.DataFrame(df_train_arr_scld,columns=df_train.columns)\n",
    "df_test_arr_scld = sc.transform(df_test)\n",
    "df_test_scld=pd.DataFrame(df_test_arr_scld,columns=df_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4m785xRv2Jf4",
    "outputId": "18cdca9f-7884-44ac-f29e-ae04150d3402"
   },
   "outputs": [],
   "source": [
    "# Spitting the data into Test and Train from combined_set_scld\n",
    "\n",
    "train = df_train_scld # Up to the last initial training set row\n",
    "test = df_test_scld # Past the last initial training set row\n",
    "\n",
    "#train = df_train # Up to the last initial training set row\n",
    "#test = df_test # Past the last initial training set row\n",
    "labels = y_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4m785xRv2Jf4",
    "outputId": "18cdca9f-7884-44ac-f29e-ae04150d3402"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>namelen</th>\n",
       "      <th>Location_Ahmedabad</th>\n",
       "      <th>Location_Bangalore</th>\n",
       "      <th>Location_Chennai</th>\n",
       "      <th>...</th>\n",
       "      <th>is_premium_I</th>\n",
       "      <th>is_premium_P</th>\n",
       "      <th>Seats_2.0</th>\n",
       "      <th>Seats_4.0</th>\n",
       "      <th>Seats_5.0</th>\n",
       "      <th>Seats_6.0</th>\n",
       "      <th>Seats_7.0</th>\n",
       "      <th>Seats_8.0</th>\n",
       "      <th>Seats_9.0</th>\n",
       "      <th>Seats_10.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.099422</td>\n",
       "      <td>0.368090</td>\n",
       "      <td>1.937179</td>\n",
       "      <td>-1.036946</td>\n",
       "      <td>-1.009167</td>\n",
       "      <td>-0.652672</td>\n",
       "      <td>-0.523170</td>\n",
       "      <td>-0.196657</td>\n",
       "      <td>-0.251542</td>\n",
       "      <td>-0.298769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.643324</td>\n",
       "      <td>-0.643324</td>\n",
       "      <td>-0.05164</td>\n",
       "      <td>-0.12935</td>\n",
       "      <td>0.436555</td>\n",
       "      <td>-0.07197</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>-0.150935</td>\n",
       "      <td>-0.022336</td>\n",
       "      <td>-0.028841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.510872</td>\n",
       "      <td>-0.434202</td>\n",
       "      <td>0.266755</td>\n",
       "      <td>-0.063648</td>\n",
       "      <td>0.254823</td>\n",
       "      <td>-0.211941</td>\n",
       "      <td>0.733173</td>\n",
       "      <td>-0.196657</td>\n",
       "      <td>-0.251542</td>\n",
       "      <td>-0.298769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.643324</td>\n",
       "      <td>-0.643324</td>\n",
       "      <td>-0.05164</td>\n",
       "      <td>-0.12935</td>\n",
       "      <td>0.436555</td>\n",
       "      <td>-0.07197</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>-0.150935</td>\n",
       "      <td>-0.022336</td>\n",
       "      <td>-0.028841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.777363</td>\n",
       "      <td>-0.304800</td>\n",
       "      <td>0.028123</td>\n",
       "      <td>-0.701958</td>\n",
       "      <td>-0.451524</td>\n",
       "      <td>-0.526749</td>\n",
       "      <td>-1.779514</td>\n",
       "      <td>-0.196657</td>\n",
       "      <td>-0.251542</td>\n",
       "      <td>3.347064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.643324</td>\n",
       "      <td>-0.643324</td>\n",
       "      <td>-0.05164</td>\n",
       "      <td>-0.12935</td>\n",
       "      <td>0.436555</td>\n",
       "      <td>-0.07197</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>-0.150935</td>\n",
       "      <td>-0.022336</td>\n",
       "      <td>-0.028841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.455304</td>\n",
       "      <td>0.756296</td>\n",
       "      <td>0.505387</td>\n",
       "      <td>-0.620294</td>\n",
       "      <td>-0.451524</td>\n",
       "      <td>-0.400826</td>\n",
       "      <td>-1.151342</td>\n",
       "      <td>-0.196657</td>\n",
       "      <td>-0.251542</td>\n",
       "      <td>3.347064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.643324</td>\n",
       "      <td>-0.643324</td>\n",
       "      <td>-0.05164</td>\n",
       "      <td>-0.12935</td>\n",
       "      <td>-2.290665</td>\n",
       "      <td>-0.07197</td>\n",
       "      <td>2.812933</td>\n",
       "      <td>-0.150935</td>\n",
       "      <td>-0.022336</td>\n",
       "      <td>-0.028841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.133245</td>\n",
       "      <td>-0.442742</td>\n",
       "      <td>-0.687772</td>\n",
       "      <td>0.579662</td>\n",
       "      <td>0.515056</td>\n",
       "      <td>1.362099</td>\n",
       "      <td>0.607539</td>\n",
       "      <td>-0.196657</td>\n",
       "      <td>-0.251542</td>\n",
       "      <td>-0.298769</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.554426</td>\n",
       "      <td>1.554426</td>\n",
       "      <td>-0.05164</td>\n",
       "      <td>-0.12935</td>\n",
       "      <td>0.436555</td>\n",
       "      <td>-0.07197</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>-0.150935</td>\n",
       "      <td>-0.022336</td>\n",
       "      <td>-0.028841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 257 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Kilometers_Driven   Mileage    Engine     Power  New_Price  \\\n",
       "0 -1.099422           0.368090  1.937179 -1.036946 -1.009167  -0.652672   \n",
       "1  0.510872          -0.434202  0.266755 -0.063648  0.254823  -0.211941   \n",
       "2 -0.777363          -0.304800  0.028123 -0.701958 -0.451524  -0.526749   \n",
       "3 -0.455304           0.756296  0.505387 -0.620294 -0.451524  -0.400826   \n",
       "4 -0.133245          -0.442742 -0.687772  0.579662  0.515056   1.362099   \n",
       "\n",
       "    namelen  Location_Ahmedabad  Location_Bangalore  Location_Chennai  \\\n",
       "0 -0.523170           -0.196657           -0.251542         -0.298769   \n",
       "1  0.733173           -0.196657           -0.251542         -0.298769   \n",
       "2 -1.779514           -0.196657           -0.251542          3.347064   \n",
       "3 -1.151342           -0.196657           -0.251542          3.347064   \n",
       "4  0.607539           -0.196657           -0.251542         -0.298769   \n",
       "\n",
       "      ...      is_premium_I  is_premium_P  Seats_2.0  Seats_4.0  Seats_5.0  \\\n",
       "0     ...          0.643324     -0.643324   -0.05164   -0.12935   0.436555   \n",
       "1     ...          0.643324     -0.643324   -0.05164   -0.12935   0.436555   \n",
       "2     ...          0.643324     -0.643324   -0.05164   -0.12935   0.436555   \n",
       "3     ...          0.643324     -0.643324   -0.05164   -0.12935  -2.290665   \n",
       "4     ...         -1.554426      1.554426   -0.05164   -0.12935   0.436555   \n",
       "\n",
       "   Seats_6.0  Seats_7.0  Seats_8.0  Seats_9.0  Seats_10.0  \n",
       "0   -0.07197  -0.355501  -0.150935  -0.022336   -0.028841  \n",
       "1   -0.07197  -0.355501  -0.150935  -0.022336   -0.028841  \n",
       "2   -0.07197  -0.355501  -0.150935  -0.022336   -0.028841  \n",
       "3   -0.07197   2.812933  -0.150935  -0.022336   -0.028841  \n",
       "4   -0.07197  -0.355501  -0.150935  -0.022336   -0.028841  \n",
       "\n",
       "[5 rows x 257 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4m785xRv2Jf4",
    "outputId": "18cdca9f-7884-44ac-f29e-ae04150d3402",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6016, 257)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4m785xRv2Jf4",
    "outputId": "18cdca9f-7884-44ac-f29e-ae04150d3402"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1234, 257)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4m785xRv2Jf4",
    "outputId": "18cdca9f-7884-44ac-f29e-ae04150d3402"
   },
   "outputs": [],
   "source": [
    "# Keep one set of train data for further use\n",
    "train1=train.copy()\n",
    "test1=test.copy()\n",
    "y_train1=y_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4m785xRv2Jf4",
    "outputId": "18cdca9f-7884-44ac-f29e-ae04150d3402"
   },
   "outputs": [],
   "source": [
    "#train=train1\n",
    "#test=test1\n",
    "#y_train=y_train1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's start building models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
    "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "# Define a cross validation strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Validation function\n",
    "n_folds = 5\n",
    "\n",
    "def rmsle_cv(model):\n",
    "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n",
    "    rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
    "    return(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmsle(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensembling LGBM, XGBoost and Gradient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final Training and Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "# Gradient Boosting Regression :\n",
    "With huber loss that makes it robust to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
    "                                   max_depth=4,\n",
    "                                   min_samples_leaf=15, min_samples_split=15, \n",
    "                                   loss='huber', random_state =5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "GBoost.fit(train, y_train)\n",
    "gb_train_pred = GBoost.predict(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_pred = (GBoost.predict(test.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7306934682038493\n"
     ]
    }
   ],
   "source": [
    "print(rmsle(y_train, gb_train_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "# XGBoost : :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgb = xgb.XGBRegressor(colsample_bytree=0.52, gamma=0.03, \n",
    "                             learning_rate=0.072, max_depth=6, \n",
    "                             min_child_weight=2, n_estimators=2200,\n",
    "                             reg_alpha=0, reg_lambda=1,\n",
    "                             subsample=0.615, silent=1,\n",
    "                             random_state =7, nthread = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3112781978405923\n"
     ]
    }
   ],
   "source": [
    "model_xgb.fit(train, y_train)\n",
    "xgb_train_pred = model_xgb.predict(train)\n",
    "xgb_pred = model_xgb.predict(test)\n",
    "print(rmsle(y_train, xgb_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCs9o0xt3Zfi"
   },
   "outputs": [],
   "source": [
    "price_XG_pred = pd.DataFrame(xgb_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCs9o0xt3Zfi",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "price_XG_pred.to_excel('price_test_reslt_xgb1.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "# Light GBM : :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=35,\n",
    "                              learning_rate=0.052, n_estimators=2800,feature_fraction = 0.75,\n",
    "                              feature_fraction_seed=9,min_data_in_leaf =15\n",
    "                                                         \n",
    "                              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lgb.fit(train, y_train)\n",
    "lgb_train_pred = model_lgb.predict(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2841241979007227\n"
     ]
    }
   ],
   "source": [
    "print(rmsle(y_train, lgb_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_pred = model_lgb.predict(test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCs9o0xt3Zfi"
   },
   "outputs": [],
   "source": [
    "price_pred_lgb = pd.DataFrame(lgb_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCs9o0xt3Zfi"
   },
   "outputs": [],
   "source": [
    "price_pred_lgb.to_excel('price_test_reslt_lgbm.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "ens_train =  xgb_train_pred*0.35 + lgb_train_pred*0.35 + gb_train_pred*0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCs9o0xt3Zfi"
   },
   "outputs": [],
   "source": [
    "ens_train_pd = pd.DataFrame(ens_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCs9o0xt3Zfi"
   },
   "outputs": [],
   "source": [
    "ens_train_pd.to_excel('ens_train_pd.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE score on train data:\n",
      "0.5579649364702558\n"
     ]
    }
   ],
   "source": [
    "\n",
    "'''RMSE on the entire Train data when averaging'''\n",
    "\n",
    "print('RMSLE score on train data:')\n",
    "print(rmsle(y_train,xgb_train_pred*0.45 + lgb_train_pred*0.3 + gb_train_pred*0.25 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ensemble = stacked_pred*0.4 + xgb_pred*0.3 + gb_pred*0.3-->85\n",
    "\n",
    "ensemble =  xgb_pred*0.4 + lgb_pred*0.3 + gb_pred*0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.68323106,  3.17106095, 17.50420147, ...,  3.14830948,\n",
       "        5.10074144, 16.81826322])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCs9o0xt3Zfi"
   },
   "outputs": [],
   "source": [
    "price_pred_ensb = pd.DataFrame(ensemble)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCs9o0xt3Zfi",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "price_pred_ensb.to_excel('price_test_reslt_ensb1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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